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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1925))

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Abstract

Inspired by the recent successes of deep learning on Computer Vision, we propose a deep learning-based system for Automatic License Plate Recognition (ALPR). The recognition system has two main modules: license plate detection (LPD) and license plate recognition (LPR). We employ anchor clustering, generalized IoU, and focal loss for improving YOLO based license plate detection and a method to generate synthesis license places to improve character recognition. The experiments on UFPR-ALPR and VN-ALPR datasets show that our recognition system achieved 94.06% and 96.00% for the ALPR task, respectively. Moreover, our recognition system achieves real-time processing at 30–32 FPS.

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References

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Acknowledgment

We thank Dr. Dung Duc Nguyen, Institute of Information Technology, Vietnam and Rayson Laroca, Vision, Robotics, and Imaging Research Group, the Federal University of Parana for providing the VN-ALPR and UFPR-ALPR datasets, respectively.

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Correspondence to Anh Le .

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Le, A., Pham, D., Lam, T. (2023). Robust and Accurate Automatic License Plate Recognition System. In: Dang, T.K., KĂĽng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_44

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  • DOI: https://doi.org/10.1007/978-981-99-8296-7_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8295-0

  • Online ISBN: 978-981-99-8296-7

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